00000cam 2200000zi 4500 9.966663 CaOODSP 20261009130542 m o d f cr cn||||||||| 261009e20250908onc ob f000 0 eng d CaOODSP eng rda CaOODSP n-cn--- CS11-522/2024-1-19E-PDF Ather, Hanan, author. Efficient record linkage for large datasets by business names / by Hanan Ather, Serge Godbout, and Dave MacNeil. At head of title: Proceedings of Statistics Canada Symposium 2024 : the future of official statistics [Ottawa] : Statistics Canada = Statistique Canada, September 8, 2025. 1 online resource (8 unnumbered pages). text txt rdacontent computer c rdamedia online resource cr rdacarrier [Statistics Canada international symposium series : proceedings], 1709-8211 Title from cover. Issued also in French under title: Couplage efficace d'enregistrements pour les grands ensembles de données, selon les noms d'entreprise. Includes bibliographical references. "Accurate and efficient record linkage is crucial for maintaining a comprehensive and current Statistical Business Register (SBR) at Statistics Canada. Linking external business lists to the SBR by name presents computational and methodological challenges, especially as data volumes grow. This paper describes a scalable methodology that employs blocking techniques to constrain the computational search space and integrates multiple similarity measures - from edit distances and n-gram overlaps to embedding-based methods using Sentence-BERT (SBERT) - to identify likely matches. By combining simple character-level comparisons with more advanced semantic embedding methods, the approach can adapt to various naming conventions and complexities. While it does not guarantee superior accuracy in all circumstances, it offers a pragmatic balance between computational feasibility and linkage quality"--Abstract, page [2]. Data integration (Computer science) Canada. Big data Canada. Deep learning (Machine learning) Statistics Canada, issuing body. Couplage efficace d'enregistrements pour les grands ensembles de données, selon les noms d'entreprise / (CaOODSP)9.966664 Statistics Canada international symposium series - proceedings, 1709-8211 (CaOODSP)9.505681 PDF 297 KB https://publications.gc.ca/collections/collection_2026/statcan/11-522-x/CS11-522-2024-1-19-eng.pdf 11-522-X